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Ant Colony Optimization Based Orthogonal Directional Proactive–Reactive Routing Protocol for Wireless Sensor Networks

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Routing protocols for wireless sensor networks are important in addressing the various quality-of-service (QoS) issues pertaining to different applications. The most important QoS issues while designing routing protocols for WSN are energy awareness, scalability and network lifetime. However to deal with these issues the solutions provided in related literature have certain inherent disadvantages like high control overhead, low packet delivery ratio and requirement of global location information. In order to resolve these issues, we propose an orthogonal transmission based scalable, lightweight and energy aware routing protocol named as OD-PPRP which does not require global location information and has low control overhead. The proposed protocol OD-PRRP has the characteristics of both reactive and proactive routing protocols and utilizes fuzzy logic and Ant Colony Optimization to identify energy efficient and optimal paths. The simulation results show in both static and dynamic environment, OD-PRRP has better network lifetime, low end to end transmission delay, less overhead and high packet delivery ratio than other state of art QoS aware routing protocol viz. EARQ, EAODV and EEABR.

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  1. 1.

    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

  2. 2.

    Elson, J., & Römer, K. (2003). Wireless sensor networks: A new regime for time synchronization. ACM SIGCOMM Computer Communication Review, 33(1), 149–154.

  3. 3.

    Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

  4. 4.

    Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3), 325–349.

  5. 5.

    Cheng, B. N., Yuksel, M., & Kalyanaraman, S. (2009). Orthogonal rendezvous routing protocol for wireless mesh networks. IEEE/ACM Transactions on Networking (ToN), 17(2), 542–555.

  6. 6.

    Lee, S., Choe, H., Park, B., Song, Y., & Kim, C. K. (2011). LUCA: An energy-efficient unequal clustering algorithm using location information for wireless sensor networks. Wireless Personal Communications, 56(4), 715–731.

  7. 7.

    Perkins, C. E., & Royer, E. M. (1999, February). Ad hoc on-demand distance vector routing. In Second IEEE workshop on mobile computing systems and applications, 1999. Proceedings. WMCSA’99. IEEE, pp. 90–100.

  8. 8.

    Dorigo, M. (Ed.) (2006). Ant colony optimization and swarm intelligence: 5th international workshop, ANTS 2006, Brussels, Belgium, September 47, 2006, Proceedings (vol. 4150). Springer.

  9. 9.

    Heo, J., Hong, J., & Cho, Y. (2009). EARQ: Energy aware routing for real-time and reliable communication in wireless industrial sensor networks. IEEE Transactions on Industrial Informatics, 5(1), 3–11.

  10. 10.

    Li, W., Chen, M., & Li, M. M. (2009). An enhanced AODV route protocol applying in the wireless sensor networks. In Fuzzy information and engineering volume 2. Berlin Heidelberg: Springer, pp. 1591–1600.

  11. 11.

    Camilo, T., Carreto, C., Silva, J. S., & Boavida, F. (2006). An energy-efficient ant-based routing algorithm for wireless sensor networks. In Ant colony optimization and swarm intelligence. Berlin Heidelberg: Springer, pp. 49–59.

  12. 12.

    Perkins, C. E., & Bhagwat, P. (1994, October). Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers. In ACM SIGCOMM computer communication review, vol. 24(4). ACM, pp. 234–244.

  13. 13.

    Clausen, T., Jacquet, P., Adjih, C., Laouiti, A., Minet, P., Muhlethaler, P., & Viennot, L. (2001). Optimized link state routing protocol (OLSR). In IEEE INMIC’01, pp. 151–162.

  14. 14.

    Ogier, R., Templin, F., & Lewis, M. (2003). Topology dissemination based on reverse-path forwarding (TBRPF): Correctness and simulation evaluation. Technical report, SRI International, Menlo Park, California.

  15. 15.

    Johnson, D. B., & Maltz, D. A. (1996). Dynamic source routing in ad hoc wireless networks. In Mobile computing. US: Springer, pp. 153–181.

  16. 16.

    Lee, S.-J., Gerla, M., & Chiang, C.-C. (1999, September). On-demand multicast routing protocol. In Proceedings of IEEE WCNC.

  17. 17.

    Yu, J. Y., Chong, P. H. J., & Zhang, M. (2008, September). Performance of efficient CBRP in mobile ad hoc networks (MANETS). In Vehicular technology conference, 2008. VTC 2008-Fall. IEEE 68th. IEEE, pp. 1–7.

  18. 18.

    Karp, B., & Kung, H. T. (2000, August). GPSR: Greedy perimeter stateless routing for wireless networks. In Proceedings of the 6th annual international conference on mobile computing and networking. ACM, pp. 243–254.

  19. 19.

    Beijar, N. (2002). Zone routing protocol (ZRP). Finland: Networking Laboratory, Helsinki University of Technology.

  20. 20.

    Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., & Silva, F. (2003). Directed diffusion for wireless sensor networking. IEEE/ACM Transactions on Networking, 11(1), 2–16.

  21. 21.

    Sohrabi, K., Gao, J., Ailawadhi, V., & Pottie, G. J. (2000). Protocols for self-organization of a wireless sensor network. IEEE Personal Communications, 7(5), 16–27.

  22. 22.

    Chang, J. H., & Tassiulas, L. (2004). Maximum lifetime routing in wireless sensor networks. IEEE/ACM Transactions on Networking (TON), 12(4), 609–619.

  23. 23.

    Li, G., Wang, L., & Li, H. (2008, April). Multiple-objective fuzzy decision making based routing protocol for wireless sensor networks. In IEEE international conference on networking, sensing and control, 2008. ICNSC 2008. IEEE, pp. 1273–1278.

  24. 24.

    Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.

  25. 25.

    Minhas, M. R., Gopalakrishnan, S., & Leung, V. C. (2009, April). An online multipath routing algorithm for maximizing lifetime in wireless sensor networks. In Sixth international conference on information technology: New generations, 2009. ITNG’09. IEEE, pp. 581–586.

  26. 26.

    Jabbar, S., Minhas, A. A., Akhtar, R. A., & Aziz, M. Z. (2009, December). REAR: Real-time energy aware routing for wireless adhoc micro sensors network. In Eighth IEEE international conference on dependable, autonomic and secure computing, 2009 (DASC’09). IEEE, pp. 825–830.

  27. 27.

    AlShawi, I. S., Yan, L., Pan, W., & Luo, B. (2012). Lifetime enhancement in wireless sensor networks using fuzzy approach and A-star algorithm. IEEE Sensors Journal, 12(10), 3010–3018.

  28. 28.

    Ortiz, A. M., Olivares, T., Castillo, J. C., Orozco-Barbosa, L., Marrón, P. J., & Royo, F. (2010, October). Intelligent role-based routing for dense wireless sensor networks. In 2010 third joint IFIP wireless and mobile networking conference (WMNC). IEEE, pp. 1–6.

  29. 29.

    Ahvar, E., Pourmoslemi, A., & Piran, M. J. (2011). Fear: A fuzzy-based energy-aware routing protocol for wireless sensor networks. arXiv preprint arXiv:1108.2777.

  30. 30.

    Ding, N., & Liu, P. X. (2005). A centralized approach to energy-efficient protocols for wireless sensor networks. In 2005 IEEE international conference on mechatronics and automation vol. 3. IEEE, pp. 1636–1641.

  31. 31.

    Wen, Y. F., Chen, Y. Q., & Pan, M. (2008). Adaptive ant-based routing in wireless sensor networks using Energy* Delay metrics. Journal of Zhejiang University SCIENCE A, 9(4), 531–538.

  32. 32.

    Liu, Y., Zhu, H., Xu, K., & Jia, Y. (2007, August). A routing strategy based on ant algorithm for WSN. In Third international conference on natural computation, 2007. ICNC 2007, vol. 5. IEEE, pp. 685–689.

  33. 33.

    Rappaport, T. S. (1996). Wireless communications: Principles and practice (Vol. 2). New Jersey: Prentice Hall PTR.

  34. 34.

    Koczy, L. T. (1992). Fuzzy graphs in the evaluation and optimization of networks. Fuzzy sets and systems, 46, 307–319.

  35. 35.

    Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

  36. 36.

    Ross, T. (2004). Fuzzy logic with engineering applications (2nd ed.). Chichester: Wiley.

  37. 37.

    Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An Application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

  38. 38.

    Kheireddine, M., & Abdellatif, R. (2014). Analysis of hops length in wireless sensor networks. Wireless Sensor Network, 6(6), 109–117.

  39. 39.

    Correia, L. H., Macedo, D. F., Silva, D. A., dos Santos, A. L., Loureiro, A. A., & Nogueira, J. M. S. (2005, October). Transmission power control in MAC protocols for wireless sensor networks. In Proceedings of the 8th ACM international symposium on modeling, analysis and simulation of wireless and mobile systems. ACM, pp. 282–289.

  40. 40.

    Maniezzo, V., & Carbonaro, A. (2002). Ant colony optimization: An overview. In Essays and surveys in meta heuristics. Springer US, pp. 469–492.

  41. 41.

    Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing, 2(5), 483–502.

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Correspondence to Aarti Jain.

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Jain, A., Reddy, B.V.R. Ant Colony Optimization Based Orthogonal Directional Proactive–Reactive Routing Protocol for Wireless Sensor Networks. Wireless Pers Commun 85, 179–205 (2015).

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  • Ant colony optimization
  • Energy efficient routing
  • Fuzzy logistics
  • Optimal hop length
  • Orthogonal transmission
  • Wireless sensor networks